This application relates, in general, to gaming machines, and, more particularly, to an electronic gaming engine supporting multiple games and multiple users.
Casino gaming has grown rapidly in the United States. Casino gaming is experiencing similar growth throughout the world. An important segment of this developing industry is electronic games. An electronic implementation of a game requires a method for interpreting human actions as they occur within the constraints of the rules as well as the ability to respond with chance events.
Microprocessors allow games that formerly relied on analog devices for generating chance events, such as dice, to be simulated digitally. Simulating a die roll with a computer would seem to be a contradiction because the microprocessor is the embodiment of logic and determinism. With care, however, it is possible to create deterministic algorithms that produce unpredictable, statistically random numbers.
Contemporary games consist of a framework of rules that define the options for how a user or random event generator may change the game state. Play begins with an initial state. Subsequent play consists of user initiated events that trigger the execution of one or more rules. A rule may proceed deterministically or non-deterministically.
Typical games consist of deterministic and non-deterministic rules. A game progresses by the interaction of these rules. There are two sources for non-determinism player decisions and chance events. In the game of Poker, for example, deciding to replace three instead of two cards in a hand is a player decision that is limited, but not predetermined, by rules. The rules limit the range of options the player has, but within that set of options the player is free to choose. An example of a chance event is the random set of cards received by the poker player. Shuffled cards do not produce a predictable hand.
Other examples that illustrate determinism and non-determinism in gaming are popular casino pastimes such as Blackjack, Keno, and Slot machines. The first Blackjack hand a player receives is two cards from a shuffled deck. The number of cards dealt is two, but the cards could be any from the deck. Keno is essentially a lottery. In Keno, a player attempts to guess twenty balls chosen from a basket of eighty balls. The rules dictate that to participate, a player must fill out a Keno ticket indicating the balls he believes will be chosen in the next round. The selection of balls, however, is a purely random event. Slot machines require the player to pull a handle for each round. Slot wheels stop at random positions.
The non-deterministic problem in most parlor games is random sampling without replacement: given a set of n elements, randomly choose m of them without replacement where m is less than or equal to n. Although sampling without replacement covers most popular games, it would be easy to conceive of games that required replacement. For example, consider a variant of Keno that replaces each chosen ball before selecting the next ball. Until now, no device is available that services the needs of multiple games by providing algorithms for sampling with and without replacement as well as others such as random permutation generation, sorting, and searching.
A casino player must know the likelihood of winning a jackpot is commensurate with the stated theoretical probabilities of the game. Moreover, the casino would like to payout as little as possible while maximizing the number of their game participants. Because each game sponsored by a casino has a built-in theoretical edge for the house, over time and with repeated play, the house will make money. In other words, the casino does not need to cheat the customer because it has a built-in edge. The customer, who is at a disadvantage in the long run, will want to know the game is fair in order to manage risk. In is a theoretical fact that bold wagering in Roulette increases a player's odds of winning. A player who cannot know the odds of winning cannot formulate a strategy.
Provided that the deterministic rules of a game are implemented correctly, it is essential that the chance events of a game are indeed random. An important subproblem for generating random events is uniform random number generation. If the underlying uniform random number generator does not generate statistically independent and uniform pseudo-random numbers, then either the house or customer will be at a disadvantage. A poorly designed system might favor the house initially and over time turn to favor the player. Certainly the house would not want this situation because it makes revenue projection impossible. Any regulatory body would like to ensure that neither the house nor customer have an advantage beyond the stated theoretical probabilities of the game. In the context of fairly implemented rules, the only way for the house to increase its revenue is to increase the number of players participating in their games.
Typically, an engineer creating an electronic game generates a flow chart representing the rules and uses a random number generator in conjunction with combinatorial algorithms for generating chance events. Representing rules is one problem. Generating chance events to support those rules is another. Creating pseudo-random numbers is a subtle problem that requires mathematical skills distinct from other problems of gaming. In other words, a skilled game programmer may be unable to solve the problems of developing a proper random number generator. Even if given a quality random number generator, problems can occur in hardware implementations that render the generator predictable. One example is using the same seed, or initial state, for the generator at regular intervals and repeatedly generating a limited batch of numbers. Without attending to the theoretical aspects of a uniform random number generator, it is not possible to implement the rules of a game perfectly. The result is a game unfair to the house, players, or both. Hence, there is a need for a gaming system, apparatus, and method that separate the problem of implementing game rules from that of random event generation.
The need for such a device is also evident at the regulatory level. Gaming is a heavily regulated industry. States, tribes, and the federal government have gaming regulatory agencies at various levels to ensure fairness of the games. The gaming regulatory authority certifies that a particular implementations of a game reflects the underlying probabilities. Because electronic games are implemented in often difficult to understand software, the problem of verifying fairness of a game is challenging. Further, there is little uniformity in the implementation of fundamental components of various games. To determine fairness, the gaming authority subjects each game to a battery of tests. No set of statistical tests performed on a limited portion of the random number generator period can ensure that the generator will continue to perform fairly in the field. The process of testing is both expensive and of limited accuracy. Hence, a regulatory need exists for a uniform, standardized method of implementing games that reduce the need and extent of individual game testing while increasing the reliability of detecting and certifying game fairness.